Establishment of an architecture-specific experimental validation approach for finite element modeling of bone by rapid prototyping and high resolution computed tomography
Bibliographic record
Abstract
A new experimental validation method for assessing the accuracy of large-scale finite element (FE) models of bone micro-structure at the apparent and tissue level was developed. Augmented scaled bone replicas were built using rapid prototype machines based on micro-computed tomography (micro-CT) data. The geometric accuracy of the model was evaluated by comparing experimental tests with the replicas to the FE solution based on the same micro-CT data. A new version of the large-scale FE solver was developed to incorporate orthotropic material properties, hence the experimentally determined properties of the rapid prototype material were input into the FE models. The modified FE solver predicted the experimental apparent level stiffness within less than 1%, and the difference between experimental strain gauge measurements and FE-calculated surface stresses was 7% and 49% on a flat and curved surface region, respectively. While absolute error estimates of surface stresses were limited due to strain gauge errors, the relatively larger difference on the curved surface is indicative of the limitations of a hexahedron FE model for representing such geometries. Although the validation approach is applied here for hexahedron based meshes, the method is flexible for varying bone architectures and will be important for validation of future large-scale FE modeling developments that utilize techniques such as mesh smoothing and tetrahedron elements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".